Building Failure Intelligence for AI Agents

When you run AI agents in production, you quickly realize:

The dangerous failures aren’t random.
They’re recurring patterns.

Examples:

• Similar hallucination structures
• Repeated tool-call mistakes
• Prompt injection variants
• Context leakage patterns

Most tools give you logs.
Some give you tracing.
Few give you structured failure memory.

I’ve been exploring a model where:

1. Every failure becomes a canonical entity
2. A deterministic fingerprint is generated for executions
3. New executions are matched against historical failures
4. A policy engine maps confidence → allow / warn / block

The key idea:

Don’t modify the LLM.
Don’t rely only on prompts.
Insert a deterministic governance layer before execution.

This turns failure history into enforcement intelligence.

Still early, but curious:
LINK : https://github.com/prateekdevisingh/kakveda

How are others handling repeat failure patterns in agent-based systems?

opensource #llm #agents #devops #aigovernance